docs: def calc_acceleration_bca(jackknife_replicates):
      """
      Calculate the acceleration constant for the Bias Corrected and Accelerated
      (BCa) bootstrap confidence intervals.
  
      Parameters
      ----------
      jackknife_replicates : 2D ndarray.
          Each row should correspond to a different jackknife parameter sample,
          formed by deleting a particular observation and then re-estimating the
          desired model. Each column should correspond to an element of the
          parameter vector being estimated.
  
      Returns
      -------
      acceleration : 1D ndarray.
          There will be one element for each element in `mle_estimate`. Elements
          denote the acceleration factors for each component of the parameter
          vector.
  
      References
      ----------
      Efron, Bradley, and Robert J. Tibshirani. An Introduction to the Bootstrap.
          CRC press, 1994. Section 14.3, Equation 14.15.
      """
      # Get the mean of the bootstrapped statistics.
      jackknife_mean = jackknife_replicates.mean(axis=0)[None, :]
      # Calculate the differences between the mean of the bootstrapped statistics
      differences = jackknife_mean - jackknife_replicates
      numerator = (differences**3).sum(axis=0)
      denominator = 6 * ((differences**2).sum(axis=0))**1.5
      # guard against division by zero. Note that this guard shouldn't distort
      # the computational results since the numerator should be zero whenever the
      # denominator is zero.
      zero_denom = np.where(denominator == 0)
      denominator[zero_denom] = MIN_COMP_VALUE
      # Compute the acceleration.
      acceleration = numerator / denominator
      return acceleration